Comparing Human Factors for Augmented Reality Supported Single-User and Collaborative Repair Operations of Industrial Robots

Abstract

In order to support the decision-making process of industry on how to implement Augmented Reality (AR) in production, this article wants to provide guidance through a set of comparative user studies. The results are obtained from the feedback of 160 participants who performed the same repair task on a switch cabinet of an industrial robot. The studies compare several AR instruction applications on different display devices (head-mounted display, handheld tablet PC and projection-based spatial AR) with baseline conditions (paper instructions and phone support), both in a single-user and a collaborative setting. Next to insights on the performance of the individual device types for the single mode operation, the study is able to show significant indications on AR techniques are being especially helpful in a collaborative setting.

Investigating the Use of Head Mounted Devices for Remote Cooperation and Guidance during the Treatment of Wounds

“…They can manipulate the virtual on-site room and objects in it by hand-gestures or additional control devices. Areas in which such settings have been successfully applied include industrial maintenance [4,24], the provision of advice and finding objects [38,50], manual assembly tasks [18,26], crime scene investigation [17], remote furniture consultations [15,37] as well as support and training in healthcare [9,40,44] as discussed in this paper. In most of these scenarios, the remote user is some sort of helper or holds additional knowledge.…”

Effect of Body Representation Level of an Avatar on Quality of AR-Based Remote Instruction

“…The workload is the amount of effort a user must spend to accomplish a task. No standard method to evaluate the workload in a task performed using a remote collaboration system exists; some researchers have employed a self-designed questionnaire [5], although most studies have used an existing questionnaire, such as the NASA Task Load Index (NASA-TLX) [29,30,[40][41][42], SMEQ [20,39], or SEQ [20]. The performance is a fundamental aspect in evaluating a remote instruction system.…”

Augmented Reality for Human–Robot Collaboration and Cooperation in Industrial Applications: A Systematic Literature Review

“…Despite not totally mature for every industrial applications due to hardware factors such as ergonomic aspects like weight and movement restraint, which do limit the number of components and features that a visualization equipment can have, due to software factors such as tracking and recognition technology limitations, lack of standards on applications development and on how and which information should be displayed to the user, as well as due to being a new technology yet to be fully integrated into enterprise resource planning systems (ERP) and manufacturing execution systems (MES) [ 30 , 31 ], AR has been used in plenty of different industrial applications such as: Assembly support [ 32 ]; Maintenance support [ 33 ]; Remote assistance [ 34 ]; Logistics [ 35 ]; Training [ 36 ]; Quality Control [ 37 ]; Data visualization/feedback [ 38 ]; Welding [ 39 ]; Product design/authoring [ 40 ]; and Safety [ 41 ] among other applications including Human–Robot Collaboration that will be discussed next. Thus, proving that AR is a promising tool to increase the operator’s performance and safety conditions, increase activities execution speed, decrease the error rate during task execution, rework and redundant inspection, and reduce mental workload.…”